Computing depth from out-of-focus blur using a local frequency representation

Gokstorp, M.

doi:10.1109/icpr.1994.576248

Cited by 37 publications

(21 citation statements)

References 10 publications

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“…The Fourier transform gives global information, i.e., only one blur value can be calculated for the entire image. To obtain a dense blur estimate, techniques using local computation have been used, such as the short-time Fourier transform [24], Gabor filter [3], and moment filters [25].…”

Section: Related Workmentioning

confidence: 99%

Depth from Defocus Estimation in Spatial Domain

Ziou¹,

Deschênes²

2001

Computer Vision and Image Understanding

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Section: Related Workmentioning

confidence: 99%

Depth from Defocus Estimation in Spatial Domain

Ziou¹,

Deschênes²

2001

Computer Vision and Image Understanding

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“…Compared to the preceding two algorithms, DFD reconstructs depth using the concept of blurring degree of the region images [6][7][8]. Since DFD was first introduced by Pentland [5] in 1987, it has become more attractive because of the following three reasons: 1) it requires only two defocused images; 2) it avoids the process of matching and masking; 3) it has been proved to be effective in both frequency and spatial domain [9,10].…”

Section: Introductionmentioning

confidence: 99%

Global shape reconstruction of nano grid with singly fixed camera

Wei

Dong

2011

Sci. China Technol. Sci.

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Depth from defocus (DFD), as a typical shape reconstruction method, has been widely researched in most recent years. However, all the existing DFD algorithms require at least two defocused images with different camera parameters. Unfortunately, in micro/nano manipulation, any change on visual sensor's parameters is absolutely forbidden. Therefore, a novel DFD method to reconstruct the shape of a nano grid on micro/nano scale is researched in this paper. First, the blurring imaging model is constructed with the relative blurring and the diffusion equation. Second, the relationship between depth and blurring is discussed from four aspects. Subsequently, depth measurement problem is transformed into an optimization issue which is solved using the gradient flow algorithm. Finally, experiment results and error analysis are conducted to show the feasibility and effectiveness of the proposed method.depth from defocus, diffusion equation, shape reconstruction, nano grid Citation:Wei Y J, Wu C D, Dong Z L. Global shape reconstruction of nano grid with singly fixed camera.

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“…Usually, DFD algorithm captures two images obtained with different camera parameters, measures blurring degree at every point and estimates depth using the point spread function. During the past years, DFD has become attractive because (i) it requires only two images; (ii) it avoids matching and masking problems and (iii) it is effective both in the frequency domain and in the spatial domain [11,12].…”

Section: Introductionmentioning

confidence: 99%

Depth measurement using single camera with fixed camera parameters

Wei

Dong

2012

IET Comput. Vis.

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Owing to the space limitation and the strict requirement on operation, depth measurement using single visual sensor is necessary in many applications, such as mini-robot, precision processing and micro/nano-manipulation. Depth from defocus (DFD), a typical method applied in depth reconstruction, has been extensively researched and has developed greatly in recent years. However, all the existing DFD algorithms has focused only on the situation that blurring images with different camera parameters (i.e. focal length or radius of the lens), and it resulted in the inapplicability of these algorithms in cases where any change of camera parameters is absolutely forbidden. Therefore a novel DFD method considering different images with fixed camera parameters is given. First, the blurring imaging model is constructed with the relative blurring and the diffusion equation. Secondly the relation between depth and blurring is discussed. Subsequently, the depth measurement problem is transformed into an optimisation issue. Finally, simulations and experiments are conducted to show the feasibility and effectiveness of the proposed method.

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Computing depth from out-of-focus blur using a local frequency representation

Cited by 37 publications

References 10 publications

Depth from Defocus Estimation in Spatial Domain

Depth from Defocus Estimation in Spatial Domain

Global shape reconstruction of nano grid with singly fixed camera

Depth measurement using single camera with fixed camera parameters

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